Overview

Dataset statistics

Number of variables12
Number of observations7255
Missing cells138
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory680.3 KiB
Average record size in memory96.0 B

Variable types

Numeric10
Text2

Alerts

protein fat ratio has 107 (1.5%) infinite valuesInfinite
protein fat ratio has 138 (1.9%) missing valuesMissing
Grams is highly skewed (γ1 = 30.13655217)Skewed
Fiber is highly skewed (γ1 = 29.42332236)Skewed
Unnamed: 0 has unique valuesUnique
Protein has 222 (3.1%) zerosZeros
Fat has 245 (3.4%) zerosZeros
Sat.Fat has 378 (5.2%) zerosZeros
Fiber has 1726 (23.8%) zerosZeros
Carbs has 474 (6.5%) zerosZeros
protein fat ratio has 84 (1.2%) zerosZeros

Reproduction

Analysis started2023-12-10 07:18:27.943614
Analysis finished2023-12-10 07:19:33.428835
Duration1 minute and 5.49 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIQUE 

Distinct7255
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3788.1184
Minimum0
Maximum7417
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2023-12-10T12:49:33.860485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile525.7
Q11976.5
median3790
Q35603.5
95-th percentile7054.3
Maximum7417
Range7417
Interquartile range (IQR)3627

Descriptive statistics

Standard deviation2097.7533
Coefficient of variation (CV)0.55377185
Kurtosis-1.1923858
Mean3788.1184
Median Absolute Deviation (MAD)1814
Skewness-0.0054352377
Sum27482799
Variance4400569.1
MonotonicityStrictly increasing
2023-12-10T12:49:34.674045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
4982 1
 
< 0.1%
5008 1
 
< 0.1%
5007 1
 
< 0.1%
5006 1
 
< 0.1%
5005 1
 
< 0.1%
5004 1
 
< 0.1%
5003 1
 
< 0.1%
5002 1
 
< 0.1%
5001 1
 
< 0.1%
Other values (7245) 7245
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
ValueCountFrequency (%)
7417 1
< 0.1%
7416 1
< 0.1%
7415 1
< 0.1%
7414 1
< 0.1%
7413 1
< 0.1%
7412 1
< 0.1%
7411 1
< 0.1%
7410 1
< 0.1%
7409 1
< 0.1%
7408 1
< 0.1%

Food
Text

Distinct7241
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
2023-12-10T12:49:35.957279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length184
Median length110
Mean length39.240937
Min length3

Characters and Unicode

Total characters284693
Distinct characters73
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7228 ?
Unique (%)99.6%

Sample

1st rowCows' milk
2nd rowButter milk
3rd rowEvaporated, undiluted
4th rowFortified milk
5th rowPowdered milk
ValueCountFrequency (%)
with 2241
 
5.0%
or 1539
 
3.5%
and 1221
 
2.7%
fat 1055
 
2.4%
added 631
 
1.4%
sauce 619
 
1.4%
cooked 592
 
1.3%
as 587
 
1.3%
to 577
 
1.3%
ns 561
 
1.3%
Other values (1843) 34862
78.4%
2023-12-10T12:49:38.382682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37246
 
13.1%
e 28653
 
10.1%
a 22273
 
7.8%
o 17517
 
6.2%
t 17011
 
6.0%
r 16121
 
5.7%
d 13192
 
4.6%
i 13131
 
4.6%
n 12417
 
4.4%
, 12397
 
4.4%
Other values (63) 94735
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 220686
77.5%
Space Separator 37246
 
13.1%
Other Punctuation 13101
 
4.6%
Uppercase Letter 11397
 
4.0%
Dash Punctuation 1134
 
0.4%
Close Punctuation 446
 
0.2%
Open Punctuation 446
 
0.2%
Decimal Number 237
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 28653
13.0%
a 22273
 
10.1%
o 17517
 
7.9%
t 17011
 
7.7%
r 16121
 
7.3%
d 13192
 
6.0%
i 13131
 
6.0%
n 12417
 
5.6%
s 10852
 
4.9%
c 9433
 
4.3%
Other values (16) 60086
27.2%
Uppercase Letter
ValueCountFrequency (%)
C 1909
16.8%
S 1727
15.2%
P 1250
11.0%
N 969
8.5%
B 763
 
6.7%
F 737
 
6.5%
M 496
 
4.4%
R 481
 
4.2%
T 449
 
3.9%
G 386
 
3.4%
Other values (16) 2230
19.6%
Other Punctuation
ValueCountFrequency (%)
, 12397
94.6%
/ 329
 
2.5%
; 186
 
1.4%
' 74
 
0.6%
% 67
 
0.5%
" 25
 
0.2%
. 16
 
0.1%
& 6
 
< 0.1%
: 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 109
46.0%
1 74
31.2%
2 32
 
13.5%
5 9
 
3.8%
4 6
 
2.5%
3 4
 
1.7%
9 2
 
0.8%
7 1
 
0.4%
Space Separator
ValueCountFrequency (%)
37246
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1134
100.0%
Close Punctuation
ValueCountFrequency (%)
) 446
100.0%
Open Punctuation
ValueCountFrequency (%)
( 446
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 232083
81.5%
Common 52610
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 28653
12.3%
a 22273
 
9.6%
o 17517
 
7.5%
t 17011
 
7.3%
r 16121
 
6.9%
d 13192
 
5.7%
i 13131
 
5.7%
n 12417
 
5.4%
s 10852
 
4.7%
c 9433
 
4.1%
Other values (42) 71483
30.8%
Common
ValueCountFrequency (%)
37246
70.8%
, 12397
 
23.6%
- 1134
 
2.2%
) 446
 
0.8%
( 446
 
0.8%
/ 329
 
0.6%
; 186
 
0.4%
0 109
 
0.2%
' 74
 
0.1%
1 74
 
0.1%
Other values (11) 169
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 284693
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37246
 
13.1%
e 28653
 
10.1%
a 22273
 
7.8%
o 17517
 
6.2%
t 17011
 
6.0%
r 16121
 
5.7%
d 13192
 
4.6%
i 13131
 
4.6%
n 12417
 
4.4%
, 12397
 
4.4%
Other values (63) 94735
33.3%

Grams
Real number (ℝ)

SKEWED 

Distinct65
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.20028
Minimum12
Maximum1419
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2023-12-10T12:49:39.172760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile100
Q1100
median100
Q3100
95-th percentile100
Maximum1419
Range1407
Interquartile range (IQR)0

Descriptive statistics

Standard deviation26.067842
Coefficient of variation (CV)0.25758667
Kurtosis1242.3385
Mean101.20028
Median Absolute Deviation (MAD)0
Skewness30.136552
Sum734208
Variance679.5324
MonotonicityNot monotonic
2023-12-10T12:49:40.251517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 7099
97.8%
85 23
 
0.3%
250 11
 
0.2%
50 10
 
0.1%
200 6
 
0.1%
346 6
 
0.1%
110 5
 
0.1%
40 4
 
0.1%
120 4
 
0.1%
70 4
 
0.1%
Other values (55) 83
 
1.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
14 3
 
< 0.1%
16 1
 
< 0.1%
20 3
 
< 0.1%
23 3
 
< 0.1%
28 3
 
< 0.1%
30 3
 
< 0.1%
40 4
 
0.1%
50 10
0.1%
52 1
 
< 0.1%
ValueCountFrequency (%)
1419 1
 
< 0.1%
976 1
 
< 0.1%
925 1
 
< 0.1%
540 1
 
< 0.1%
454 2
 
< 0.1%
346 6
0.1%
300 1
 
< 0.1%
270 1
 
< 0.1%
260 1
 
< 0.1%
257 1
 
< 0.1%

Calories
Real number (ℝ)

Distinct5447
Distinct (%)75.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.44657
Minimum0
Maximum1373
Zeros27
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2023-12-10T12:49:41.452112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31.469
Q183.35
median165.5
Q3276.395
95-th percentile475.29
Maximum1373
Range1373
Interquartile range (IQR)193.045

Descriptive statistics

Standard deviation146.19606
Coefficient of variation (CV)0.73300865
Kurtosis2.6384451
Mean199.44657
Median Absolute Deviation (MAD)93.61
Skewness1.2833682
Sum1446984.8
Variance21373.287
MonotonicityNot monotonic
2023-12-10T12:49:42.219640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27
 
0.4%
65.33 20
 
0.3%
66.53 16
 
0.2%
65.54 16
 
0.2%
900 15
 
0.2%
66.41 15
 
0.2%
205.26 13
 
0.2%
66.04 11
 
0.2%
177.67 11
 
0.2%
65.01 10
 
0.1%
Other values (5437) 7101
97.9%
ValueCountFrequency (%)
0 27
0.4%
0.32 1
 
< 0.1%
0.4 9
 
0.1%
0.48 1
 
< 0.1%
0.52 1
 
< 0.1%
0.53 1
 
< 0.1%
0.54 1
 
< 0.1%
0.56 1
 
< 0.1%
0.6 1
 
< 0.1%
0.66 5
 
0.1%
ValueCountFrequency (%)
1373 1
 
< 0.1%
1225 1
 
< 0.1%
1100 1
 
< 0.1%
992 1
 
< 0.1%
900.26 1
 
< 0.1%
900 15
0.2%
899.73 1
 
< 0.1%
896.44 1
 
< 0.1%
895.26 1
 
< 0.1%
838.54 1
 
< 0.1%

Protein
Real number (ℝ)

ZEROS 

Distinct2116
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7129318
Minimum-1
Maximum232
Zeros222
Zeros (%)3.1%
Negative1
Negative (%)< 0.1%
Memory size56.8 KiB
2023-12-10T12:49:43.072755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0.16
Q12.22
median6.2
Q312.255
95-th percentile25.243
Maximum232
Range233
Interquartile range (IQR)10.035

Descriptive statistics

Standard deviation8.9558154
Coefficient of variation (CV)1.0278762
Kurtosis62.180572
Mean8.7129318
Median Absolute Deviation (MAD)4.52
Skewness3.9870392
Sum63212.32
Variance80.20663
MonotonicityNot monotonic
2023-12-10T12:49:44.203859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 222
 
3.1%
0.1 35
 
0.5%
2 34
 
0.5%
3 30
 
0.4%
1.4 29
 
0.4%
0.3 29
 
0.4%
0.2 29
 
0.4%
1.6 23
 
0.3%
1.38 23
 
0.3%
4 23
 
0.3%
Other values (2106) 6778
93.4%
ValueCountFrequency (%)
-1 1
 
< 0.1%
0 222
3.1%
0.01 3
 
< 0.1%
0.02 5
 
0.1%
0.03 5
 
0.1%
0.04 8
 
0.1%
0.05 4
 
0.1%
0.06 4
 
0.1%
0.07 5
 
0.1%
0.08 14
 
0.2%
ValueCountFrequency (%)
232 1
< 0.1%
114 2
< 0.1%
89 1
< 0.1%
78.13 2
< 0.1%
76.25 1
< 0.1%
66.67 1
< 0.1%
64.06 1
< 0.1%
62.82 2
< 0.1%
61.3 1
< 0.1%
58.94 1
< 0.1%

Fat
Real number (ℝ)

ZEROS 

Distinct2112
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0930172
Minimum0
Maximum233
Zeros245
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2023-12-10T12:49:45.043138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07
Q12.06
median5.55
Q312.71
95-th percentile27.993
Maximum233
Range233
Interquartile range (IQR)10.65

Descriptive statistics

Standard deviation11.606483
Coefficient of variation (CV)1.2764172
Kurtosis36.526698
Mean9.0930172
Median Absolute Deviation (MAD)4.61
Skewness4.2214346
Sum65969.84
Variance134.71045
MonotonicityNot monotonic
2023-12-10T12:49:45.745099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 245
 
3.4%
0.1 77
 
1.1%
0.2 51
 
0.7%
0.3 32
 
0.4%
0.08 31
 
0.4%
3.49 31
 
0.4%
0.02 29
 
0.4%
1 29
 
0.4%
0.07 26
 
0.4%
3.41 25
 
0.3%
Other values (2102) 6679
92.1%
ValueCountFrequency (%)
0 245
3.4%
0.01 18
 
0.2%
0.02 29
 
0.4%
0.03 13
 
0.2%
0.04 14
 
0.2%
0.05 11
 
0.2%
0.06 11
 
0.2%
0.07 26
 
0.4%
0.08 31
 
0.4%
0.09 15
 
0.2%
ValueCountFrequency (%)
233 1
 
< 0.1%
115 2
 
< 0.1%
110 1
 
< 0.1%
100 16
0.2%
99.98 1
 
< 0.1%
99.97 1
 
< 0.1%
99.48 1
 
< 0.1%
99.1 1
 
< 0.1%
92.18 1
 
< 0.1%
87.34 1
 
< 0.1%

Sat.Fat
Real number (ℝ)

ZEROS 

Distinct3504
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9873482
Minimum0
Maximum234
Zeros378
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2023-12-10T12:49:46.628796image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median1.465
Q33.757
95-th percentile10.1295
Maximum234
Range234
Interquartile range (IQR)3.257

Descriptive statistics

Standard deviation5.6242402
Coefficient of variation (CV)1.8826865
Kurtosis466.55984
Mean2.9873482
Median Absolute Deviation (MAD)1.305
Skewness14.975202
Sum21673.211
Variance31.632077
MonotonicityNot monotonic
2023-12-10T12:49:47.220405image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 378
 
5.2%
0.002 40
 
0.6%
0.4 19
 
0.3%
0.009 18
 
0.2%
1 18
 
0.2%
0.014 18
 
0.2%
1.429 17
 
0.2%
0.02 17
 
0.2%
0.018 17
 
0.2%
1.24 16
 
0.2%
Other values (3494) 6697
92.3%
ValueCountFrequency (%)
0 378
5.2%
0.001 9
 
0.1%
0.002 40
 
0.6%
0.003 10
 
0.1%
0.004 8
 
0.1%
0.005 9
 
0.1%
0.006 12
 
0.2%
0.007 5
 
0.1%
0.008 14
 
0.2%
0.009 18
 
0.2%
ValueCountFrequency (%)
234 1
 
< 0.1%
116 2
< 0.1%
92 1
 
< 0.1%
88 1
 
< 0.1%
82.5 1
 
< 0.1%
61.924 1
 
< 0.1%
51.368 3
< 0.1%
46.24 1
 
< 0.1%
46.235 1
 
< 0.1%
45.39 3
< 0.1%

Fiber
Real number (ℝ)

SKEWED  ZEROS 

Distinct164
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7558112
Minimum0
Maximum235
Zeros1726
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2023-12-10T12:49:47.983861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median1
Q32.1
95-th percentile6.3
Maximum235
Range235
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.269807
Coefficient of variation (CV)2.4318145
Kurtosis1385.9865
Mean1.7558112
Median Absolute Deviation (MAD)1
Skewness29.423322
Sum12738.41
Variance18.231252
MonotonicityNot monotonic
2023-12-10T12:49:48.761114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1726
23.8%
0.6 313
 
4.3%
1.2 214
 
2.9%
1 207
 
2.9%
0.2 204
 
2.8%
0.9 203
 
2.8%
0.4 201
 
2.8%
1.1 196
 
2.7%
0.7 190
 
2.6%
0.3 188
 
2.6%
Other values (154) 3613
49.8%
ValueCountFrequency (%)
0 1726
23.8%
0.1 175
 
2.4%
0.2 204
 
2.8%
0.3 188
 
2.6%
0.31 1
 
< 0.1%
0.4 201
 
2.8%
0.5 138
 
1.9%
0.6 313
 
4.3%
0.7 190
 
2.6%
0.8 184
 
2.5%
ValueCountFrequency (%)
235 1
< 0.1%
117 2
< 0.1%
67.5 1
< 0.1%
46.2 1
< 0.1%
42.8 1
< 0.1%
37.5 1
< 0.1%
37 1
< 0.1%
29.3 1
< 0.1%
27.3 2
< 0.1%
26.9 1
< 0.1%

Carbs
Real number (ℝ)

ZEROS 

Distinct3137
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.943391
Minimum0
Maximum236
Zeros474
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2023-12-10T12:49:49.394746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.565
median13.29
Q326.405
95-th percentile71.64
Maximum236
Range236
Interquartile range (IQR)20.84

Descriptive statistics

Standard deviation22.468601
Coefficient of variation (CV)1.0728254
Kurtosis4.7151198
Mean20.943391
Median Absolute Deviation (MAD)9.15
Skewness1.7861498
Sum151944.3
Variance504.83802
MonotonicityNot monotonic
2023-12-10T12:49:50.104341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 474
 
6.5%
0.1 88
 
1.2%
12.51 36
 
0.5%
13.1 30
 
0.4%
9.85 30
 
0.4%
6.87 23
 
0.3%
9.86 16
 
0.2%
1 15
 
0.2%
7.45 15
 
0.2%
0.2 14
 
0.2%
Other values (3127) 6514
89.8%
ValueCountFrequency (%)
0 474
6.5%
0.01 3
 
< 0.1%
0.03 2
 
< 0.1%
0.04 3
 
< 0.1%
0.05 2
 
< 0.1%
0.06 9
 
0.1%
0.07 5
 
0.1%
0.08 3
 
< 0.1%
0.09 12
 
0.2%
0.1 88
 
1.2%
ValueCountFrequency (%)
236 1
< 0.1%
229 1
< 0.1%
216 1
< 0.1%
199 1
< 0.1%
154 1
< 0.1%
119 1
< 0.1%
118 2
< 0.1%
100 2
< 0.1%
99.77 1
< 0.1%
99.6 2
< 0.1%
Distinct2445
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
2023-12-10T12:49:51.194068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length79
Median length62
Mean length14.102274
Min length3

Characters and Unicode

Total characters102312
Distinct characters71
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1754 ?
Unique (%)24.2%

Sample

1st rowDairy products
2nd rowDairy products
3rd rowDairy products
4th rowDairy products
5th rowDairy products
ValueCountFrequency (%)
or 665
 
4.0%
with 664
 
4.0%
and 494
 
3.0%
chicken 475
 
2.9%
sandwich 297
 
1.8%
egg 285
 
1.7%
rice 271
 
1.6%
beef 265
 
1.6%
sauce 234
 
1.4%
potato 229
 
1.4%
Other values (1347) 12697
76.6%
2023-12-10T12:49:53.543172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 10829
 
10.6%
9324
 
9.1%
a 8608
 
8.4%
r 6416
 
6.3%
o 6116
 
6.0%
t 5786
 
5.7%
i 5410
 
5.3%
s 5143
 
5.0%
n 4769
 
4.7%
c 3758
 
3.7%
Other values (61) 36153
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 83780
81.9%
Space Separator 9324
 
9.1%
Uppercase Letter 8355
 
8.2%
Other Punctuation 311
 
0.3%
Open Punctuation 185
 
0.2%
Close Punctuation 181
 
0.2%
Dash Punctuation 162
 
0.2%
Decimal Number 14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10829
12.9%
a 8608
 
10.3%
r 6416
 
7.7%
o 6116
 
7.3%
t 5786
 
6.9%
i 5410
 
6.5%
s 5143
 
6.1%
n 4769
 
5.7%
c 3758
 
4.5%
l 3693
 
4.4%
Other values (16) 23252
27.8%
Uppercase Letter
ValueCountFrequency (%)
C 1766
21.1%
P 1095
13.1%
B 753
9.0%
S 735
8.8%
F 486
 
5.8%
M 448
 
5.4%
T 411
 
4.9%
R 339
 
4.1%
G 277
 
3.3%
E 247
 
3.0%
Other values (16) 1798
21.5%
Other Punctuation
ValueCountFrequency (%)
, 180
57.9%
' 62
 
19.9%
/ 37
 
11.9%
" 22
 
7.1%
& 6
 
1.9%
% 2
 
0.6%
; 1
 
0.3%
: 1
 
0.3%
Decimal Number
ValueCountFrequency (%)
0 4
28.6%
2 3
21.4%
3 2
14.3%
5 2
14.3%
1 1
 
7.1%
9 1
 
7.1%
7 1
 
7.1%
Space Separator
ValueCountFrequency (%)
9324
100.0%
Open Punctuation
ValueCountFrequency (%)
( 185
100.0%
Close Punctuation
ValueCountFrequency (%)
) 181
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 162
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 92135
90.1%
Common 10177
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10829
 
11.8%
a 8608
 
9.3%
r 6416
 
7.0%
o 6116
 
6.6%
t 5786
 
6.3%
i 5410
 
5.9%
s 5143
 
5.6%
n 4769
 
5.2%
c 3758
 
4.1%
l 3693
 
4.0%
Other values (42) 31607
34.3%
Common
ValueCountFrequency (%)
9324
91.6%
( 185
 
1.8%
) 181
 
1.8%
, 180
 
1.8%
- 162
 
1.6%
' 62
 
0.6%
/ 37
 
0.4%
" 22
 
0.2%
& 6
 
0.1%
0 4
 
< 0.1%
Other values (9) 14
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10829
 
10.6%
9324
 
9.1%
a 8608
 
8.4%
r 6416
 
6.3%
o 6116
 
6.0%
t 5786
 
5.7%
i 5410
 
5.3%
s 5143
 
5.0%
n 4769
 
4.7%
c 3758
 
3.7%
Other values (61) 36153
35.3%

Calorie per gram
Real number (ℝ)

Distinct5502
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9965625
Minimum0
Maximum9.0181818
Zeros27
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2023-12-10T12:49:54.569132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.31231
Q10.8292
median1.655
Q32.76875
95-th percentile4.80455
Maximum9.0181818
Range9.0181818
Interquartile range (IQR)1.93955

Descriptive statistics

Standard deviation1.462092
Coefficient of variation (CV)0.73230464
Kurtosis1.7860001
Mean1.9965625
Median Absolute Deviation (MAD)0.939
Skewness1.1946321
Sum14485.061
Variance2.1377129
MonotonicityNot monotonic
2023-12-10T12:49:55.580897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27
 
0.4%
0.6533 20
 
0.3%
0.6554 16
 
0.2%
0.6653 16
 
0.2%
9 15
 
0.2%
0.6641 15
 
0.2%
2.0526 13
 
0.2%
1.7767 11
 
0.2%
0.6604 11
 
0.2%
3.273 10
 
0.1%
Other values (5492) 7101
97.9%
ValueCountFrequency (%)
0 27
0.4%
0.0032 1
 
< 0.1%
0.004 9
 
0.1%
0.0048 1
 
< 0.1%
0.0052 1
 
< 0.1%
0.0053 1
 
< 0.1%
0.0054 1
 
< 0.1%
0.0056 1
 
< 0.1%
0.006 1
 
< 0.1%
0.0066 5
 
0.1%
ValueCountFrequency (%)
9.018181818 1
 
< 0.1%
9.0026 1
 
< 0.1%
9 15
0.2%
8.9973 1
 
< 0.1%
8.9644 1
 
< 0.1%
8.9526 1
 
< 0.1%
8.928571429 3
 
< 0.1%
8.3854 1
 
< 0.1%
8.22 1
 
< 0.1%
7.8794 1
 
< 0.1%

protein fat ratio
Real number (ℝ)

INFINITE  MISSING  ZEROS 

Distinct5237
Distinct (%)73.6%
Missing138
Missing (%)1.9%
Infinite107
Infinite (%)1.5%
Meaninf
Minimum-0.14285714
Maximuminf
Zeros84
Zeros (%)1.2%
Negative1
Negative (%)< 0.1%
Memory size56.8 KiB
2023-12-10T12:49:56.644461image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-0.14285714
5-th percentile0.17768301
Q10.56469356
median1.1391231
Q32.6415929
95-th percentile11.331328
Maximuminf
Rangeinf
Interquartile range (IQR)2.0768994

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Meaninf
Median Absolute Deviation (MAD)0.74166907
Skewnessnan
Suminf
Variancenan
MonotonicityNot monotonic
2023-12-10T12:49:57.540806image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
inf 107
 
1.5%
0 84
 
1.2%
1 49
 
0.7%
2 44
 
0.6%
3 23
 
0.3%
4 20
 
0.3%
0.5 16
 
0.2%
0.3784530387 16
 
0.2%
5 16
 
0.2%
0.4340175953 15
 
0.2%
Other values (5227) 6727
92.7%
(Missing) 138
 
1.9%
ValueCountFrequency (%)
-0.1428571429 1
 
< 0.1%
0 84
1.2%
0.001100220044 1
 
< 0.1%
0.001982406145 2
 
< 0.1%
0.002613776138 1
 
< 0.1%
0.002814636108 1
 
< 0.1%
0.002842334058 4
 
0.1%
0.003320357225 1
 
< 0.1%
0.00625798212 3
 
< 0.1%
0.007766470273 1
 
< 0.1%
ValueCountFrequency (%)
inf 107
1.5%
506 1
 
< 0.1%
296.5 2
 
< 0.1%
110 1
 
< 0.1%
77 1
 
< 0.1%
73.5 1
 
< 0.1%
64.11764706 1
 
< 0.1%
63.94117647 2
 
< 0.1%
61.75 1
 
< 0.1%
58.66666667 1
 
< 0.1%

Interactions

2023-12-10T12:49:26.503091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:35.565824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:41.942868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:47.321256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:52.731091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:59.240598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:04.620788image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:10.079675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:15.277265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:20.379616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:27.078961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:36.192403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:42.566780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:47.872204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:53.410512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:59.737632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:05.143051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:10.589343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:15.745643image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:21.162838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:27.680702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:36.709045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:43.033412image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:48.378055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:54.380749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:00.435318image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:05.750547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:11.157528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:16.332085image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:22.330919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:28.203045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:37.451145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:43.512081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:48.743393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:55.147150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:00.871652image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:06.242185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:11.763203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:16.880632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:22.912075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:28.721565image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:38.251019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:44.041439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:49.231474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:55.840017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:01.222731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:06.791578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:12.255674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:17.341212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:23.459642image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:29.250442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:39.017815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:44.663913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:50.075784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:56.443088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:01.668765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:07.337794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:12.874241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:17.769657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:24.029645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:29.746617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:39.717565image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:45.249307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:50.702490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:57.122195image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:02.104543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:07.790339image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:13.349598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:18.179430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:24.555615image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:30.222169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:40.233580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:45.719975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:51.216516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:57.756675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:02.619648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:08.419070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:13.845789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:18.637735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:24.960778image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:30.702010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:40.700209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:46.344608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:51.689302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:58.302217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:03.430768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:08.958832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:14.339915image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:19.152385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:25.497973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:31.220672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:41.242396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:46.880414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:52.242171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:48:58.745501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:04.100415image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:09.501963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:14.826537image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:19.850709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-10T12:49:26.004636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2023-12-10T12:49:31.909703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T12:49:32.843787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0FoodGramsCaloriesProteinFatSat.FatFiberCarbsCategoryCalorie per gramprotein fat ratio
00Cows' milk976660.032.040.036.00.048.0Dairy products0.6762300.800000
12Butter milk246127.09.05.04.00.013.0Dairy products0.5162601.800000
23Evaporated, undiluted252345.016.020.018.00.024.0Dairy products1.3690480.800000
34Fortified milk14191373.089.042.023.01.4119.0Dairy products0.9675832.119048
45Powdered milk103515.027.028.024.00.039.0Dairy products5.0000000.964286
58Goats' milk244165.08.010.08.00.011.0Dairy products0.6762300.800000
69(1/2 cup ice cream)540690.024.024.022.00.070.0Dairy products1.2777781.000000
710Cocoa252235.08.011.010.00.026.0Dairy products0.9325400.727273
811skim. milk250128.018.04.03.01.013.0Dairy products0.5120004.500000
912(cornstarch)248275.09.010.09.00.040.0Dairy products1.1088710.900000
Unnamed: 0FoodGramsCaloriesProteinFatSat.FatFiberCarbsCategoryCalorie per gramprotein fat ratio
72457408Cauliflower, cooked, as ingredient10031.332.000.290.1352.15.18Cauliflower0.31336.896552
72467409Eggplant, cooked, as ingredient10031.191.050.190.0373.26.32Eggplant0.31195.526316
72477410Green beans, cooked, as ingredient10038.751.910.230.0522.87.26Green beans0.38758.304348
72487411Summer squash, cooked, as ingredient10024.831.310.350.1041.24.11Summer squash0.24833.742857
72497412Dark green vegetables as ingredient in omelet10037.002.970.400.0782.55.38Dark green vegetables as ingredient in omelet0.37007.425000
72507413Tomatoes as ingredient in omelet10028.431.110.230.0381.65.48Tomatoes as ingredient in omelet0.28434.826087
72517414Other vegetables as ingredient in omelet10036.503.460.380.0611.44.81Other vegetables as ingredient in omelet0.36509.105263
72527415Vegetables as ingredient in curry10055.351.810.190.0512.211.60Vegetables as ingredient in curry0.55359.526316
72537416Sauce as ingredient in hamburgers100279.571.3422.853.5440.617.14Sauce as ingredient in hamburgers2.79570.058643
72547417Industrial oil as ingredient in food100900.000.00100.0032.6720.00.00Industrial oil as ingredient in food9.00000.000000